System and methods for managing blood loss of a patient

ABSTRACT

One variation of the method for managing blood loss of a patient includes: receiving an image of a physical sample; extracting a feature from an area of the image corresponding to the physical sample; estimating a blood volume indicator of the physical sample according to the extracted feature; estimating a patient blood loss based on the blood volume indicator; estimating a euvolemic patient hematocrit based on an estimated patient blood volume and the estimated patient blood loss; receiving a measured patient hematocrit; and generating a volemic status indicator based on a comparison between the measured patient hematocrit and the estimated euvolemic patient hematocrit.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/943,561, filed on Apr. 2, 2018, which is a divisional of U.S. patentapplication Ser. No. 13/894,054, filed on May 14, 2013, which alsoclaims the benefit of U.S. Provisional Patent Application No.61/776,577, filed on Mar. 11, 2013, U.S. Provisional Patent ApplicationNo. 61/646,822, filed on May 14, 2012, and U.S. Provisional PatentApplication No. 61/722,780, filed on Nov. 5, 2012, all of which areincorporated herein in their entireties by this reference.

This application is related to U.S. patent application Ser. No.13/544,646, filed on Jul. 9, 2012, and to U.S. patent application Ser.No. 13/738,919, filed on Jan. 10, 2013, both of which are incorporatedherein in their entireties by this reference.

TECHNICAL FIELD

This invention relates generally to the surgical field, and morespecifically to a new and useful system and method for managing bloodloss of a patient for use in surgical practice.

BACKGROUND

Overestimation and underestimation of patient blood loss is asignificant contributor to high operating and surgical costs forhospitals, clinics and other medical facilities. Specifically,overestimation of patient blood loss results in wasted transfusion-gradeblood and higher operating costs for medical institutions and can leadto blood shortages. Underestimation of patient blood loss is a keycontributor of delayed resuscitation and transfusion in the event ofhemorrhage and has been associated with billions of dollars in avoidablepatient infections, re-hospitalizations, and lawsuits annually.Uninformed estimation of varying patient hematocrit during hemorrhage,blood transfusion, and intravenous saline infusion further exacerbatesinaccurate estimation of patient red blood cell loss and negativelyimpacts the timing and quantity of fluids supplied to a patientintravenously. Thus, there is a need in the surgical field for a new anduseful system and method for managing blood loss of a patient. Thisinvention provides such a new and useful system and method.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a flowchart representation of a first method;

FIG. 2 is a flowchart representation of one variation of the firstmethod;

FIG. 3 is a flowchart representation of the first method;

FIG. 4 is a flowchart representation of one variation of the firstmethod;

FIG. 5 is a flowchart representation of one variation of the firstmethod;

FIGS. 6A and 6B area schematic representations in accordance with onevariation of the first method;

FIG. 7 is a graphical representations in accordance with one variationof the first method;

FIG. 8 is a graphical representations in accordance with one variationof the first method;

FIG. 9 is a schematic representation of a system of one embodiment;

FIG. 10 is a schematic representation of a variation of the system;

FIG. 11 is a flowchart representation of a second method;

FIG. 12 is a flowchart representation of one variation of the secondmethod;

FIG. 13 is a flowchart representation of one variation of the secondmethod;

FIG. 14 is a graphical representation of one variation of the secondmethod; and

FIG. 15 is a graphical representation of one variation of the secondmethod.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

The following description of preferred embodiments of the invention isnot intended to limit the invention to these preferred embodiments, butrather to enable any person skilled in the art to make and use thisinvention.

1. First Method

As shown in FIG. 1, method S100 for managing blood loss of a patientincludes: tracking a quantity of a fluid administered to the patientintravenously in Block S110; receiving an image of a physical sample inBlock S120; extracting a feature from an area of the image correlatedwith the physical sample in Block S130; estimating a red blood cellcontent of the physical sample based on the extracted feature in BlockS140; estimating an extracorporeal blood content of the physical samplebased on the estimated red blood cell content of the physical sample inBlock S150; and estimating a hematocrit of the patient based on aprevious hematocrit of the patient, the quantity of the fluidadministered to the patient, and the estimated extracorporeal bloodcontent of the physical sample in Block S160.

As shown in FIG. 2, one variation of first method S100 for managingblood loss of a patient includes: receiving an image of a first physicalsample at a first time in Block S120; estimating a first red blood cellcontent of the first physical sample based on a feature extracted fromthe image of the first physical sample in Block S140; estimating anintravascular hematocrit of the patient substantially current to thefirst time based on an initial intravascular hematocrit of the patient,the estimated first red blood cell content of the first physical sample,and a quantity of fluid administered to the patient intravenously up tothe first time in Block S160; receiving an image of a second physicalsample at a second time in Block S220; estimating a second red bloodcell content of the second physical sample based on a feature extractedfrom the image of the second physical sample in Block S240; andestimating an intravascular hematocrit of the patient substantiallycurrent to the second time based on the estimated intravascularhematocrit of the patient substantially current to the first time, theestimated second red blood cell content of the second physical sample,and a quantity of fluid administered to the patient, and the estimatedextracorporeal blood content of the physical sample in Block S260.

Hematocrit (HCT) (packed cell volume (PCV), erythrocyte volume fraction(EVF)) is the volume percentage of red blood cells in blood and isrelated to intravascular red blood cell count (RBC) and plasma volume(PF) according to the formula:

${HCT} = {\frac{RBC}{VB} = \frac{RBC}{{RBC} + {PV}}}$

wherein the total volume of blood (VB) is the sum of RBC and PVHemoglobin is the primary oxygen-transport metalloprotein in blood andmakes up most (e.g., 98%) of the dry-weight of a red blood cell.Hematocrit is therefore a fundamental indicator of the oxygen-carryingcapacity of blood and can be indicative patient fluid needs, such asintravenous infusion or transfusion of saline, blood, plasma, red bloodcells in solution, etc.

Generally, first method S100 functions to estimate the intravascularhematocrit of a patient based on fluids lost by and fluids administeredto the patient over time. First method S100 implements machine visiontechniques to analyze images of physical samples, such as a surgicalgauze sponge, a surgical towel, a surgical dressing, a surgical suctioncanister, a cell salvage canister, or a surgical drape, to estimate thequantity or volume of red blood cells (and therefore blood) lost by thepatient. First method S100 also tracks intravenous administration offluids, such as crystalloids (e.g., saline) or colloids (e.g., blood orblood components), over time, thereby maintaining a substantiallycurrent estimate of patient hematocrit based on fluids lost from andadded to the patient's circulatory system over time. First method S100can further account for other variables in estimating a currentintravascular hematocrit of the patient, such as an initial known orestimated intravascular hematocrit, an estimated extracorporeal redblood cell mass or volume, an estimated extracorporeal hemoglobin massor volume, and/or any intravenous fluid infusions or transfusionsprovided to the patient. Generally, by accounting for any of thesevariables overtime, first method S100 can maintain a substantiallyreliable estimate of patient intravascular hematocrit.

During a medical event, blood loss reduces intravascular blood volume,and, to compensate, patients are often initially administered a salinedrip or other intravenous non-blood (i.e., crystalloid) fluid to boostintravascular fluid volume. However, as the blood of the patient isdiluted with saline or other non-blood-based fluid, the oxygen-carryingcapacity of the patient's circulatory system diminishes, as indicated byreduced patient hematocrit. Patient risk increases with reduced bloodvolume and/or reduced red blood cell count per volume of blood, andfirst method S100 can therefore be useful in a hospital, medical clinic,or other medical facility or setting to track and maintain patientintravascular hematocrit. For example, first method S100 can beapplicable in a surgical setting or during a childbirth in which apatient bleeds, wherein saline is administered to the patient tomaintain intravascular (e.g., intravenous) volume and wherein a bloodcomponent (e.g., red blood cells) is subsequently administered toprevent excessive loss of intravascular oxygen-carrying capacity. Firstmethod S100 can therefore track the patient's intravascular fluid fluxto maintain a current estimate of the patient's hematocrit, therebyenabling a user (e.g., a surgeon, anesthesiologist, nurse) to avoidover- and under-administration of fluids that could otherwise lead tohypovolemia, hypervolemia, and/or related complications.

First method S100 can additionally or alternatively function to estimatetotal blood, red blood cell, or hemoglobin loss of the patient overtime, to detect the presence of blood in a sample (e.g., surgical gauzesponge, a surgical suction canister), to compute blood spread rate, tocalculate blood surface area, to estimate patient risk level (e.g.,hypovolemic shock), to determine patient hemorrhage classification, toprovide warnings, to suggest administration of or automaticallyadminister necessary fluids intravenously (e.g., blood, saline), or toprovide any other functionality.

As shown in FIGS. 1 and 2, first method S100 can be implemented by acomputer system as a hematocrit estimation system that receivestransfusion and/or infusion data, analyzes photographic images ofbloodied samples to estimate patient blood loss, and updates anestimated patient hematocrit according to the transfusion/infusion data,the estimated patient blood loss, and/or other related information. Thecomputer system can be cloud-based (e.g., Amazon EC3), a mainframecomputer system, a grid-computer system, or any other suitable computersystem. First method S100 can be implemented by a handheld (i.e.,mobile) computing device, such by a smartphone, digital music player, ortablet computer executing a native hematocrit estimation application,such as shown in FIG. 2. For example, a camera integral with thecomputing device can capture an image of the physical sample, and aprocessor integral within the computing device can implement BlocksS110, S120, S130, etc. Additionally or alternatively, the computingdevice can communicate with a remote server, such as over the Internetvia a wireless connection, wherein the server performs at least someBlocks of first method S100 and wherein at least some of the outputs offirst method S100 are transmitted back to the computing device forfurther analysis and/or subsequent release to a user. The computingdevice can also include or be coupled to a digital display such thatfirst method S100 can display information to a user (e.g., a nurse oranesthesiologist) through the display. For example, a display integralwith the computing device can display the estimated intravascularhematocrit, an estimated total patient blood loss, a hypovolemia orhypovolemia warning, a suggestion to administer a particular fluidintravenously, or any other relevant information. However, first methodS100 can be implemented in or by any other computing device, system, orcombination thereof.

As shown in FIG. 3, Block S110 of first method S100 recites tracking aquantity of a fluid administered to the patient intravenously.Generally, Block S110 functions to collect time-dependent datapertaining to patient blood, saline, and/or other infusions and/ortransfusions, which can enable first method S100 to estimate changes inthe volume and composition of fluid in the patient's circulatory systemwhen coupled with data pertaining to the contents of infused and/ortransfused fluids. Block S110 can therefore track administration ofcrystalloids (e.g., saline), colloids (e.g., blood), and/or otherfluids, whether administered exclusively or simultaneously.

Block S110 can monitor intravenous administration of fluid(s) byrecording an initial time of administration of the fluid and trackingthe quantity of the fluid administered to the patient according to atransfusion rate. In one example implementation, the computer systemimplementing first method S100 can include an interface through which auser can enter data related to a fluid infusion or transfusion. In thisexample implementation, the interface can guide the user to enter thetype of fluid (e.g., saline, plasma, red blood cells, whole blood), afluid flow rate (e.g., 100 mL/min of warmed blood), a transfusion orinfusion start time, and/or an initial fluid volume in an IV bag, asshown in FIG. 1. By integrating the flow rate over time up to the totalinitial volume of the IV bag, Block S110 can track the volume of fluidintravenously administered to the patient via the IV bag. In thisexample implementation, Block S110 can therefore track one or morefluids administered to the patient substantially indirectly.

In another example implementation, the computer system implementingfirst method S100 can include a flow sensor coupled to an IV line,wherein Block S110 monitors the volume of fluid administered to thepatient based on an output of the flow sensor. For example, the flowsensor can be an optical flow sensor arranged over a metering block atthe outlet of an IV bag. Therefore, in this example implementation,Block S110 can track one or more fluids administered to the patientsubstantially directly.

Block S110 can also receive data pertaining to the contents (i.e.,composition) of infused or transfused fluid, such as through theinterface of the computer system described above. Alternatively, BlockS110 can implement machine vision to determine the contents of an IVbag, as described in U.S. Provisional Patent Application No. 61/722,780and U.S. patent application Ser. No. 13/544,646.

In one example implementation, Block S110 further interfaces with anoptical sensor to capture an image of an IV bag, such as an allogeneicblood transfusion bag, a blood component (e.g., plasma, red blood cell)bag, or a saline therapy IV bag, and analyzes the image of the IV bag toestimate the composition of fluid therein. For example, Block S110 canreceive a second image of the blood transfusion bag, extract acolor-related feature from an area of the second image correlated withthe blood transfusion bag, and estimate a red blood cell content of theblood transfusion bag based on the color-related feature. In thisexample, Block S110 can implement a parametric or non-parametric modelto correlate the extracted (color) feature with a red blood cellconcentration, as described in U.S. patent application Ser. No.13/544,646 and U.S. patent application Ser. No. 13/738,919. Block S110can further implement machine vision techniques, such as edge detectionand/or template matching, to determine the type of IV bag, the level offluid in the IV bag, and thus the volume of fluid in the IV bag. Fromthis data, Block S110 can estimate the red blood cell count, plasmavolume, and/or hematocrit of blood in the IV bag.

In the forgoing example implementation, Block S110 can further implementmachine vision techniques to determine the type of bag in the image. Forexample, Block S110 can identify a bag with a rectangular perimeter as ablood transfusion bag and can identify a bag with a circular perimeteras a urethral catheter bag, wherein the blood transfusion bag is markedas containing fluid flowing into the patient and the urethral catheterbag is marked as containing fluid flowing out of the patient.

Alternatively, Block S110 can implement machine vision to scan abarcode, printed or embossed text, or handwritten text on the bag toidentify the type and/or contents of the bag. In an example in which thebag is a blood transfusion bag, Block S110 can read a barcode on asticker on the bag, access a database including the barcode, andretrieve bag type and/or content-related information from the databasebased on the barcode. In this example, first method S100 can thus alsolog entry of the blood transfusion bag into the operating room, checkthe blood type within the bag against the patient's blood type, capturean initial infusion time, and/or track blood inventory in an operatingor delivery room.

Therefore, Block S110 can track intravenous administration of acrystalloid fluid, a colloid fluid, or any other suitable fluid. BlockS110 can also receive the composition of an administered fluid, such asfrom a nurse or anesthesiologist, determine the composition ofadministered fluid directly by analyzing an image of the fluid, ordetermine the composition of administered fluid indirectly by accessingIV bag data, such as from a remote server or database. However, BlockS110 can function in any other way to track a quantity of a fluidadministered to the patient intravenously.

As shown in FIG. 3, Block S120 of first method S100 recites receiving animage of a physical sample. As described above and shown in FIG. 4,Block S120 can similarly recite receiving a first image of a firstphysical sample at a first time, and Block S240 can recite receiving asecond image of a second physical sample at a second time. Generally,Block S120 functions to collect an image of the physical sample at aknown time (e.g., the first time) and/or to collect an image of thephysical sample with a known time of use of the physical sample. BlockS220 similarly functions to collect a second image of a second physicalsample at a later time (e.g., the second time) such that Block S160 andBlock S260 can generate updated hematocrit estimates over time. The(first) physical sample (and second physical sample) can be any of asurgical gauze sponge, a surgical towel, a surgical dressing, a surgicalsuction canister, a cell salvage canister, or any other absorbenttextile or vessel used to collect blood or other bodily fluids during asurgery, delivery, or other medical event. However, the physical samplecan additionally or alternatively be a droplet, drop, or pool of bloodon a ground, table, wall, or floor surface, a piece of clothing, anexternal skin surface, a surgical glove, a surgical implement or tool,or any other surface, material, or object. The image of the physicalsample can be a static, single-frame image including at least a portionof the physical sample, a multi-frame video feed including multiplestatic images of the fluid canister, a color image, a black and whiteimage, a grayscale image, an infrared image, a field of view of anoptical sensor, a fingerprint of a field of view of an optical sensor, apoint cloud, or any other suitable type of image. The image can becaptured by an optical sensor, such as a digital color camera, an RGBcamera, or any number of charge-coupled device (CCD) sensors,complimentary metal-oxide-semiconductor (CMOS) active pixel sensors, orany other optical sensor of any other type. For example, the opticalsensor can be arranged within a handheld computing device implementingfirst method S100, adjacent an operating table, or over a deliverytable.

As shown in FIG. 5, one variation of first method S100 includes BlockS122, which recites capturing the image of the physical sample. BlockS122 can interface with a camera or other suitable optical sensor tocapture the image of a field of view of the camera or optical sensor,wherein the physical sample is in the field of view of the camera oroptical sensor. Block S122 can capture the image that is a static,single-frame image including at least a portion of the physical sample.Alternatively, Block S122 can capture the image that is a multi-framevideo feed including multiple static images of the fluid canister.

As described in U.S. patent application Ser. No. 13/738,919, in oneimplementation in which the physical sample is a suction canister, acell salvage canister, or other vessel, Block S122 can capture the imageof the vessel according to a time schedule, such as every thirty secondsor every two minutes. Alternatively, Block S122 can implement machinevision and/or machine recognition techniques to identify the vesselwithin the field of view of the optical sensor and trigger image captureonce a vessel is detected. For example, Block S122 can capture an imageof the field of view of the optical sensor each time a user holds thecamera (e.g., the computing device that incorporates the camera) up tothe vessel. Similarly, Block S122 can capture the image of the physicalsample once a threshold increase in the fluid volume of the vessel isdetected. Therefore, Block S122 can capture images of the physicalsample automatically, such as based on a timer, changes in fluid volumeof the vessel, or availability of the vessel for imaging, which canenable first method S100 to track fluid collection in the vessel overtime.

As described in U.S. patent application Ser. No. 13/544,646, in oneimplementation in which the physical sample is a surgical sponge gauze,Block S122 can implement machine vision and/or machine recognitiontechniques to identify the surgical sponge gauze in the field of view ofthe optical sensor and automatically capture the image of the surgicalsponge gauze. Alternatively, in the foregoing implementations, BlockS122 can capture the image of the physical sample according to a manualinput, such as from a nurse or anesthesiologist.

Block S122 can also timestamp the image of the physical sample, such asbased on when the image was captured, when the physical sample that is asurgical sponge gauze was used, and/or when the physical sample that isa fluid suction canister was filled, replaced, and/or emptied. BlockS122 can thus enable first method S100 to track changes in both thepatient's intravascular fluid volume and hematocrit based on a series ofsubsequent images, wherein an extracorporeal blood volume estimate foreach image can be based on both an estimated red blood cell content ofthe physical sample in a particular image and a hematocrit estimated fora time approximately the same as that noted in the timestamp of theparticular image. However, Block S122 can function in any other way tocapture the image of the physical sample. Block S122 can also capturethe second image of the second physical sample, as shown in FIG. 2.

Block S130 of first method S100 recites extracting a feature from anarea of the image correlated with the physical sample. Generally, BlockS130 functions to identify and select a portion of the image that isindicative of the hemoglobin mass (or red blood cell content, red bloodcell count, red blood cell volume, red blood cell mass, etc.) of thephysical sample. In one example implementation, because red blood cellsare red, Block S130 extracts the portion of the image by isolating asubstantially red region of the image. In another exampleimplementation, Block S130 implements object recognition to identity thephysical sample in the image and to remove a background from the image.For example, Block S130 can implement object localization, segmentation(e.g., edge detection, background subtraction, grab-cut-basedalgorithms, etc.), gauging, clustering, pattern recognition, templatematching, feature extraction, descriptor extraction (e.g., extraction oftexton maps, color histograms, HOG, SIFT, etc.), feature dimensionalityreduction (e.g., PCA, K-Means, linear discriminant analysis, etc.),feature selection, thresholding, positioning, color analysis, parametricregression, non-parametric regression, unsupervised or semi-supervisedparametric or non-parametric regression, or any other type of machinelearning and/or machine vision technique to select the representativearea of the image. Block S130 can further identify the type of physicalsample and/or estimate a physical dimension of the physical sample basedon the selected area.

Block S130 can extract the feature, from one or more pixels of theimage, that is a color (red), a color intensity (e.g., redness value), aluminosity, a hue, a saturation value, a brightness value, a glossvalue, or other color-related value in one or more component spaces,such as the red, blue, green, cyan, magenta, yellow, key, and/or Labcomponent spaces. Block S130 can alternatively extract the feature thatis a numerical color identifier, such as a HEX code value (e.g.,#FF0000, #A00000, #880000, etc.) or an RGB code value (e.g., (255, 0,0), (160, 0, 0), (190, 0, 0), etc.). Block S130 can also extract one ormore features that is a histogram of various color or color-relatedvalues in a set of pixels within the image. As shown in FIGS. 6A and 6B,Block S130 can also extract the feature that is a cluster of pixelswithin the selected area and correlated with a portion of the physicalsample than contains blood, such as a cluster of pixels that can becompared to template images in a library of template images of known redblood cell content. However, Block S130 can extract any other suitablefeature from one or more pixels within the image.

In one example implementation, Block S130 extracts a color intensityvalue of a set of pixels within an area of the image correlated with thephysical sample such that Block S140 can estimate the red blood cellcontent of the physical sample by implementing a parametric model totransform the color intensity value into a quantity of red blood cellsin the physical sample. In another example implementation, Block S130extracts a subset of pixels from a set of pixels within an area of theimage correlated with the physical sample such that Block S140 canestimate the red blood cell content of the physical sample by matchingthe subset of pixels with a template image in a library of templateimages of known red blood cell content, as described in U.S. patentapplication Ser. No. 13/544,646.

Therefore, as shown in FIGS. 6A and 6B, Block S130 can extract featuresfrom multiple pixels within the image to amass a set of featuresindicative of fluid quality over an area of the image correlated withthe physical sample or a portion of the physical sample that containsblood. For example, Block S130 can segment a portion of the image intom-pixel by n-pixel clusters of pixels, wherein an o by p array of pixelclusters substantially fills a selected area of the image. Block S130can then analyze each pixel cluster to extract one feature per pixelcluster. Block S130 can further average or otherwise combine featuresfrom the pixel clusters to extract a single feature indicative of thecomposition of the physical sample from the image. In another example,Block S130 can segment the selected area into non-overlappingsingle-pixel-thickness (horizontal) rows extending across the full widthof an area of the image correlated with the physical sample. In thisexample, Block S130 can average pixel properties in each row of pixelsto extract a single feature from each row. Similarly, Block S130 cansegment the selected area into three-pixel-thickness row sets extendingacross the full width of a selected area of the image, wherein the outersingle rows of each row set (except the lowermost and uppermost rowsets) are shared with adjacent row sets, and wherein the pixels in eachrow set are averaged to extract a single feature from a set of pixels.Block S130 can additionally or alternatively segment the selected areainto non-overlapping triangular pixel clusters, overlapping cross-shapedfive-pixel arrays (shown in FIGS. 6A and 6B), overlapping circular pixelclusters, or any other suitable shape and number of overlapping and/ordiscrete pixel clusters and, from these pixel clusters, extract one ormore of the same or different types of features from the set of pixels.Block S130 can alternatively extract a feature from each individualpixel in the selected area or extract any other number of features inany other way from information stored in pixels of the image bounded byan area of the image correlated with the physical sample.

Therefore, Block S130 can extract from the image one or more featuresindicative of red blood cell content of the physical sample, such asdescribed in U.S. patent application Ser. No. 13/544,646. However, BlockS130 can function in any other way to extract a feature of any othersuitable type or format from an area of the image correlated with thephysical sample. Block S130 can also extract a feature of any suitabletype or format from an area of the second image correlated with thesecond physical sample.

As shown in FIG. 3, Block S140 of first method S100 recites estimating ared blood cell content of the physical sample based on the extractedfeature. As shown in FIG. 4, Block S240 can similarly recite estimatinga second red blood cell content of the second physical sample based on afeature extracted from the second image. Generally, Block S140 functionsto transform one or more features extracted from the image extracted inBlock S130 into an estimate of the red blood cell content of thephysical sample. For example, Block S140 can implement parametricanalysis techniques and/or non-parametric analysis techniques, such asdescribed in U.S. patent application Ser. No. 13/544,646, to estimatethe red blood cell content of the physical sample.

In one implementation, Block S130 extracts features from pixel clustersin the image, and Block S140 tags each pixel cluster with a red bloodcell content based on a non-parametric correlation of each pixel clusterwith a template image in a library of template images of known red bloodcell contents. For example, Block S130 can extract a color intensity inthe red component space from a set of pixel clusters, and Block S140 canimplement a K-nearest neighbor method to compare each extracted featurewith redness intensity values of template images. In this example, eachtemplate image can include a pixel cluster tagged with a known fluidquality, such as hemoglobin or red blood cell volume or mass per pixelunit (e.g., hemoglobin or red blood concentration). Once a suitablematch between a particular pixel cluster and a particular template imageis found, Block S140 can project known red blood cell information fromthe particular template image onto the particular pixel cluster. BlockS140 can then aggregate, average, and/or otherwise combine pixel clustertags to output a total red blood cell content for the physical sample.However, Block S140 can correlate the extracted features with a redblood cell content via any other suitable non-parametric method ortechnique.

In the foregoing implementation, Block S130 can segment the portion ofthe image correlated with the physical sample, and Block S140 can matcheach segment with a template image to estimate the total red blood cellcontent of the physical sample. For example, Block S130 can segment theimage statically according to predefined segment size and/or shape, suchas a square ten-pixel by ten-pixel area. Alternatively, Block S130 cansegment the image dynamically, such as according to redness, hue,saturation, shade, brightness, chroma, wavelength range, or any othermetric or color of light in the image. Block S130 can also decomposeeach segment of the image (an ‘image segment’) into separate colorcomponents, such as a histogram of color intensity in the red, green,and blue color spaces, as shown in FIG. 6B. For at least the red colorcomponent, Block S140 can then calculate the absolute difference inpixel intensities for pixels in the image segments and pixels in thetemplate images to match each image segment with a suitable templateimage. Each template image can be contained within a the library oftemplate images, and each template image can be an image of a mastersample of known hemoglobin or red blood cell mass, volume, or density.Generally, each template image can include a red blood cell contentindicator that, when matched with the image segment, can be convertedinto a red blood cell blood content for a portion of the physical samplecorrelated with the image segment. For example, Block S140 can correlatethe image segment with a physical dimension of the physical sample byimplementing a gauging technique and then determine the type of physicalsample to be a surgical sponge gauze or surgical towel by implementingan object recognition technique. In this example, Block S140 can thenidentify an absorbency of the sample according to a database ofabsorbency values for different types of samples. Block S140 can finallypass the red blood cell content indicator of the matched template image,the absorbency of the physical sample, and the physical area of theportion of the sample through an algorithm to estimate the red bloodcell content of the portion of the physical sample. Block S140 canfinally estimate the total red blood cell content of the physical sampleby summing each red blood cell content estimate of all segments of theimage correlated with the physical sample. Alternatively, the estimatedred blood cell content indicators for all of the image segments can besummed and then manipulated according to an overall dimension and/orabsorbency of the physical sample to estimate total red blood cellcontent in the physical sample.

In another implementation, Block S130 extracts features from pixelclusters from a portion of the image, and Block S140 implements aparametric model or function to tag each pixel cluster with a red bloodcell content. As described in U.S. patent application Ser. No.13/544,646, Block S140 can insert one or more extracted features fromone pixel cluster into a parametric function to substantially directlytransform the extracted feature(s) from the pixel cluster into a redblood cell content. Block S140 can then repeat this for each other pixelcluster in the area of the image correlated with the physical sample.For example, the extracted feature(s) can include any one or more of acolor intensity in the red component space, a color intensity in theblue component space, and/or a color intensity in the green componentspace. In these examples, the parametric function can be a mathematicaloperation or algorithm that relates color intensity to hemoglobin massper unit area of the related region of the physical sample. As describedin U.S. patent application Ser. No. 13/544,646, reflectance ofoxygenated hemoglobin (HbO₂) at certain wavelengths of light can beindicative of the concentration of hemoglobin. Furthermore, because thehemoglobin content of a wet (hydrated) red blood cell is typically about35%, red blood cell content can be extrapolated from the hemoglobinconcentration based on a static estimated hemoglobin content (e.g.,35%). Therefore, in another example, Block S130 can extract areflectance value at a particular wavelength of light for each pixelcluster in a set of pixel clusters in the image, and Block S140 canconvert each reflectance value into a hemoglobin concentration value byimplementing a parametric model. Block S140 can then combine thehemoglobin concentration values to estimate the total red blood cellcontent of the physical sample. Additionally or alternatively, BlockS140 can implement a lookup table, a regression model, a non-negativeleast-squares algorithm, or any other suitable algorithm, method, orparametric model to transform one or more extracted features into a redblood cell content estimate for the physical sample.

However, Block S140 can implement any other parametric and/ornon-parametric analysis of single pixels or pixel clusters within theimage to estimate the red blood cell content of the physical image.Block S240 can implement the same or similar techniques to estimate thered blood cell content of the second physical sample. A time-basedhistory of extracorporeal red blood cells can also be augmented with theoutput of Block S140 to maintain a current estimate of total patient redblood cell loss.

As shown in FIG. 3, Block S150 of first method S100 recites estimatingan extracorporeal blood content of the physical sample based on theestimated red blood cell content of the physical sample. Generally,Block S150 functions to transform the estimated red blood content of thephysical sample into a volume of blood components in the physical sampleaccording to a measured hematocrit, an estimated static hematocrit, oran estimated dynamic hematocrit of the patient. In one implementation,Block S150 transforms the estimated red blood cell content into a totalblood volume in the physical sample, including red blood cells, plasma,leukocytes, platelets, and plasma. For example, Block S150 can implementthe equation

VB=RBC+HCT.

In another implementation, Block S150 estimates a plasma volume of thephysical sample based on the estimated red blood cell content ofphysical sample. For example, Block S150 can solve for PV in theequation

${HCT} = {\frac{RBC}{VB} = {\frac{RBC}{{RBC} + {PV}}.}}$

However, Block S150 can estimate the volume of any one or more bloodcomponents based on at least the red blood cell content estimate of thephysical sample and/or according to any other formula or algorithm.

In one implementation, Block S150 implements a static hematocrit valueto convert the red blood cell content to a blood or plasma volume. Forexample, the static hematocrit value can be a previous hematocrit valuethat was measured directly (i.e., with a centrifuge that separatecomponents of a sample of the patient's blood), such as at the beginningof a surgery. In another example, the static hematocrit value can be ahematocrit value estimated based on a characteristic and/or demographicof the patient. In this example, Block S150 can interface with BlockS180 described below to access a lookup table or algorithm relatingpatient age, gender, weight, and/or height to an average or predictedhematocrit value. The lookup table or algorithm can also account for ahealth condition of the patient that may affect the patient'shematocrit, such as anemia, leukemia, myeloma, or an eosinophilicdisorder. The static hematocrit value can also be entered by a user,such as an anesthesiologist or a surgeon such as at the beginning or anyother time during a surgery, delivery, etc.

In another implementation, Block S150 implements a dynamic hematocritvalue to convert the red blood cell content to a blood or plasma volume.As described above, first method S100 can continuously and/or cyclicallyupdate the estimated hematocrit of the patient over time, such as inresponse to each subsequent image of a physical sample or every minuteas additional fluid is administered to the patient intravenously.Therefore, in this implementation, Block S150 can access a previoushematocrit estimate (i.e., estimated in a previous application of BlockS160) and apply the previous hematocrit estimate to the red blood cellcontent estimate to generate an estimate of blood volume in the physicalsample. For example, Block S150 can select a hematocrit estimate thatwas generated at approximately the same time that the image of thephysical sample was captured. This example may be particularlyapplicable to the physical sample that is a surgical suction canister.In the implementation in which Block S122 tags the image with a timestamp based on when the physical sample was used, Block S150 can selectthe hematocrit estimate that was generated at approximately the sametime that the physical sample was used. This example may be particularlyapplicable to the physical sample that is a surgical sponge gauze. BlockS150 can additionally or alternatively select a most recent hematocritestimate. However, Block S150 can select the dynamic hematocrit valueaccording to any other schedule or schema. A time-based history of bloodloss of the patient can also be augmented with the output of Block S150to maintain a current estimate of total patient blood loss over time, asshown in FIG. 7.

As shown in FIG. 5, one variation of first method S100 further includesBlock S170, which recites tracking loss of a non-blood fluid by thepatient. Generally, Block S170 functions to implement techniques similarto those of Blocks S130, S140 and/or S150 to estimate the quantity ofother fluids contained in or adsorbed by the physical sample. Forexample, Block S170 can implement machine vision techniques to identifythe presence and volume of saline, ascites, bile, irrigant saliva,gastric fluid, mucus, pleural fluid, urine, fecal matter, or any otherbodily fluid of a patient, surgical fluid, particulate, or matter in thephysical sample. Block S170 can also access a volume of irrigant used inthe surgery and, from this quantitative and qualitative data,distinguish between fluid lost by the patient and fluid collected fromirrigation. Block S170 can thus estimate a change in intracorporealwater volume of the patient, such as a change in the level of water orwater-based fluids in the gastrointestinal system. Block S160 can thenapply an algorithm to estimate the volume of water and/or electrolytesinto the patient's circulatory system over time, which can furtherinform the estimated current hematocrit of the patient in Block S160.However, Block S170 can function in any other way to track patient lossof a non-blood fluid, and first method S100 (e.g., Block S160) canimplement data tracked in Block S170 in any other suitable way.

As shown in FIG. 5, one variation of first method S100 further includesBlock S180, which recites estimating an initial hematocrit of thepatient. Generally, Block S180 functions to transform known qualitativeand/or quantitative data of the patient into an estimated initialhematocrit of the patient, such as at the start of a surgery, delivery,or other medical event. As described above, Block S180 can implement alookup table or algorithm relating patient age, gender, weight, and/orheight to an average or predicted hematocrit value. For example, theinitial patient hematocrit of a forty-year-old male can be estimated at47% and the initial patient hematocrit of a fifteen-year-old female canbe estimated to be 41%. The lookup table or algorithm can also accountfor an altitude of the patient or a health condition of the patient thatmay affect the patient's hematocrit, such as anemia, leukemia, myeloma,an eosinophilic disorder, or a fitness level. Alternatively, Block S180can receive a direct measurement of the hematocrit of the patient, suchas by separating red blood cells and plasma in a blood sample from thepatient via centrifuge and then measuring the relative volumes of theseparated materials. However, Block S180 can function in any other wayto estimate or access an initial hematocrit of the patient.

As shown in FIG. 5, one variation of first method S100 further includesBlock S182, which recites estimating an initial intravascular bloodvolume of the patient. Generally, Block S182 functions to transformknown qualitative and/or quantitative data of the patient into anestimated initial patient blood volume, such as at the start of asurgery, delivery, or other medical event. For example, Block S182 canimplement a lookup table or an algorithm to estimate the initial patientblood volume based on a weight and a height of the patient. However,Block S182 can additionally or alternatively estimate the patient'sinitial blood volume based on an age, a gender, a health condition, etc.of the patient. However, Block S182 can function in any other way toestimate the initial blood volume of the patient.

As described in U.S. patent application Ser. No. 13/544,646, firstmethod S100 can further access non-image features, such as weight of thephysical sample, a direct measurement of a volume of fluid in thephysical sample that is a canister, a clinician-estimated canister fluidvolume, a fluid volumes and/or qualities of previous physical samples,previous fluid volumes and/or qualities of the physical sample that is afluid canister, a sample counter, an ambient lighting condition, a typeor other identifier of the physical sample, directly-measured propertiesof fluid in the physical sample, a patient vital sign, a patient medicalhistory, altitude, an identity of a surgeon, a type of surgery oroperation in process, or any other suitable non-image feature. Forexample, as described below and in U.S. patent application Ser. No.13/544,646, Blocks of first method S100 can implement any of thesenon-image features to select template images for comparison with pixelclusters in the selected area, to select of a parametric model orfunction to transform the extracted feature(s) into a red blood cellcount estimate, to define alarm triggers for excess fluid loss orhematocrit change, to transform one or more extracted features into ablood quantity indicator, or to transform one or more extracted featuresinto a quantity or quality of another fluid or solid in the physicalsample. However, first method S100 can implement any of these non-imagefeatures to modify, enable, or inform any other function or Block offirst method S100.

As shown in FIG. 3, Block S160 of first method S100 recites estimatingthe hematocrit of the patient based on a previous hematocrit of thepatient, the quantity of the fluid administered to the patient, and theestimated extracorporeal blood content of the physical sample.Additionally or alternatively, Block S160 can recite estimating anintravascular hematocrit of the patient substantially current to a firsttime based on an initial intravascular hematocrit of the patient, theestimated first red blood cell content of the first physical sample, anda quantity of fluid administered to the patient intravenously up to thefirst time, as shown in FIG. 4. Furthermore, Block S260 can reciteestimating an intravascular hematocrit of the patient substantiallycurrent to a second time based on the estimated intravascular hematocritof the patient substantially current to the first time, the estimatedsecond red blood cell content of the second physical sample, and aquantity of fluid administered to the patient intravenously up to thesecond time, as also shown in FIG. 4.

Generally, Block S160 functions to combine several variables related to,directly affecting, and/or indirectly affecting a quality and/orquantity of the patient's intravascular blood volume. Such variables caninclude fluid (e.g., blood plasma, saline) administered to the patient,blood lost by the patient, non-blood fluids lost or excreted by thepatient, adsorption of fluid and electrolytes into or out of thecirculatory system, etc., any of which can be dependent on time. BlockS160 can also combine any one or more of the foregoing variables withconstants also related to intravascular blood quantity and/or quality,such as an initial patient hematocrit (e.g., from Block S180), aninitial blood volume (e.g., from Block S182), a composition of a colloidor crystalloid administered to the patient (e.g., from Block S110), etc.Block S260 can similarly function to update the hematocrit estimate ofthe patient at a later time.

Block S160 and/or Block S260 can be repeated continuously or cyclically,such as throughout a surgery, delivery, or other medical event, tomaintain a substantially current estimate of the hematocrit of thepatient, as shown in FIG. 8. For example, Block S160 can repeat (orBlock S260 can follow Block S160) in response to retrieval of eachsubsequent image of each physical sample that is a surgical spongegauze, surgical towel, etc. and/or in response to retrieval of eachsubsequent image of a physical sample that is a surgical suctioncanister, cell salvage canister, etc.

In one implementation, Block S160 (and Block S260) implements afirst-order parametric model or algorithm to generate a substantiallycurrent estimate of intravascular patient hematocrit based ontime-dependent and static variables. For example, hematocrit can be afunction of: initial intravascular hematocrit HCT_(o); initial volume ofblood in the patient VB_(o); red blood cell (or hemoglobin) lossΔRBC_(i); intravenous infusion of saline or other non-blood-based fluidΔS_(i); intravenous transfusion of blood RH and the hematocrit of thetransfused blood HCT_(B); time (t_(i)-t_(o)), such as lag in adsorptionof saline, previously infused into the patient, out of bloodstreamt_(S,lag); etc.

In one example of this implementation, Block S160 can implement thefollowing equations to estimate the intravascular hematocrit of thepatient. First, the initial red blood cell count RBC_(o) at initial timet_(o) can be estimated based on the initial intravascular hematocrit andblood volume of the patient according to the formulas:

${{HCT}_{0} = {\frac{{RBC}_{0}}{{VB}_{0}} = \frac{{RBC}_{0}}{{RBC}_{0} + {PV}_{0}}}},{and}$RBC_(o) = VB_(o) ⋅ HCT_(o).

From this, Block S160 can calculate the current intravascular hematocritof the patient at a subsequent time t_(i) according to the formula:

${{HCT}_{i} = \frac{\left( {{RBC}_{o} - {\Delta \; {RBC}_{i}}} \right)}{\left( {{RBC}_{o} - {\Delta \; {RBC}_{i}}} \right) + \left( {{PV}_{o} + {\Delta \; S_{i}}} \right)}},$

wherein ΔRBC_(i) is the change in red blood cell (or hemoglobin) countover time and includes both blood transfusion and (estimated) blood lossfrom Blocks S140 and S150, and wherein ΔS_(i) is intravenous infusion ofsaline or another non-blood-based fluid of known composition. ΔRBC_(i)can be calculated according to the following formula:

ΔRBC_(i)=−(Σ_(n=1) ^(i)RBC_(s,n))+RB×HCT_(B)×(t _(i)-t _(B,o))

wherein (Σ_(n=1) ^(t)RBC_(s,n)) is the sum of estimated red blood cellvolumes in all samples, which can be substantially correlated with thetotal estimated red blood cell loss of the patient. Furthermore, RB isthe rate of blood transfusion, HCT_(B) is the hematocrit of transfusedblood, and RB·HCT_(B)·(t_(i)-t_(B,o))) is the total estimated red bloodcell (or hemoglobin) count transfused into the blood steam of thepatient between an initial transfusion time t_(B,o) and the current timet_(i). Finally, ΔS_(i) can be calculated according to the formula.

${\Delta \; S_{i}} = \left\{ \begin{matrix}{{RS} \cdot \left( {t_{i} - t_{S,o}} \right)} & {\left( {t_{i} - t_{S,o} - t_{S,{lag}}} \right) \leq 0} \\{{{RS} \cdot \left( {t_{i} - t_{S,o}} \right)} - {{AS} \cdot \left( {{RS} \cdot \left( {t_{i} - {tS}_{o}} \right)} \right) \cdot \left( {t_{i} - t_{S,o} - t_{S,{lag}}} \right)}} & {else}\end{matrix} \right.$

wherein RS is the volume flow rate of saline or other non-blood-basedfluid administered to the patient intravenously, AS is an estimatedabsorbency rate of saline (or water) out of the bloodstream of thepatient, and t_(S,lag) is a lag in absorption of saline (or water) outof bloodstream. Block S260 can similarly estimate a subsequenthematocrit of the patient, such as at a later time t′. However, BlockS160 and/or Block S260 can estimate the intravascular hematocrit of thepatient according to any other model or algorithm.

In other implementations, Block S160 and Block S260 implement a second-,third-, fourth-, or other-order algorithm. Block S160 and Block S260 canalso account for any other static or time-dependent variable related tointravascular and/or extracorporeal blood quantity and/or quality.

As shown in FIG. 5, one variation of first method S100 further includesBlock S190, which recites tracking the hematocrit of the patient overtime according to the previous hematocrit of the patient, the estimatedhematocrit of the patient based on the estimated extracorporeal bloodcontent of the physical sample, and subsequent estimated hematocrits ofthe patient based on estimated extracorporeal blood contents ofsubsequent physical samples. Generally, Block S190 functions to assemblepast and current estimated hematocrit values of the patient into atime-dependent trendline of the patient's intravascular hematocrit.Block S190 can additionally or alternatively output a trend in totalblood loss and/or red blood cell loss of the patient over time. From anyone or more of these trends, Block S190 can predict a future rate ofblood loss, red blood cell loss, and/or change in hematocrit of thepatient (shown in FIG. 8) and thus predict a future blood-related needof the patient. For example, Block S190 can analyze trend data topredict a future time at which the intravascular hematocrit of thepatient will drop below a threshold safety level (e.g., 34%). In lightof this prediction, a surgeon, nurse, anesthesiologist, doctor, or otheruser can replace a saline drip line with a blood transfusion linesubstantially before the safety of the patient is threatened (i.e.,before the hematocrit of the patient drops below the threshold safetylevel). Block S190 can therefore analyze estimated blood quality and/orquantity data output in any one or more of the foregoing Blocks of firstmethod S100 to predict a future need of the patient and thus enable adoctor or other medical staff to make preemptive medical decisions,thereby potentially reducing patient risk. However, Block S190 canfunction in any other way to track the hematocrit of the patient overtime and/or predict a future blood-related need of the patient.

As shown in FIG. 5, one variation of first method S100 includes BlockS192, which recites triggering an alarm in response to the estimatedhematocrit of the patient falling outside a predefined range of suitablehematocrit values. Generally, Block S192 functions to interface withBlock S160, S260, and/or Block S190 to inform a user (e.g., medicalstaff) of an estimated current or predicted future blood-related valuethat falls outside of a suitable range of values. Block S192 can providean alarm (i.e., warning) provided through any of an audible alarm, avisual alarm, a digital display, etc. The alarm can be any of a warningthat the patient's hematocrit is currently too low, a warning that thepatient's hematocrit is approaching a threshold level, a warning thatthe patient has lost a threshold volume of blood or red blood cells, awarning that the patient is approaching a blood volume or blood volumepercentage loss outside of an acceptable range, a warning that currentinfusion or transfusion fluids may be improper for patient care past acertain time or infused volume, a warning of patient risk level, such asfor hypovolemic or hypervolemic shock, an estimated patient hemorrhageclassification, or any other suitable warning.

Block S192 can additionally or alternatively provide suggestions relatedto patient care, such as to start or stop intravenous transfusion orinfusion, to set a particular volume or volume flow rate of transfusedor infused fluid, to respond to a hemorrhage, to delay a surgicaloperation until a patient condition reaches an acceptable status, tospeed up a surgical operation before a patient condition reaches acertain status, or any other suitable suggestion. The warning and/orsuggestion can also account for the age, weight, gender, health, orother demographic of the patient. Block S192 can further access anelectrocardiogram (EKG) machine, an oximeter (e.g., a finger-type bloodoxygen monitor), an IV drip monitor, or other monitor or sensorconnected to the patient, or a medical record of the patient to furtherinform the warning or suggestion. For example, an oximeter connected tothe patient that shows a substantially low oxygen level in the blood ofthe patient can verify a substantially low estimate of patientintravascular hematocrit. In another example, Block S192 accesses amedical record of the patient that indicates that the patient haschronic kidney disease, which necessitates a minimum viableintravascular hematocrit level greater than that of a similar patientwithout chronic kidney disease. However, Block S192 can function in anyother way, access any other data, and provide any other warning orsuggestion.

2. Second Method

As shown in FIG. 11, method S200 for managing blood loss of a patient,includes: receiving an image of a physical sample in Block S210;extracting a feature from an area of the image corresponding to thephysical sample in Block S220; estimating a blood volume indicator ofthe physical sample according to the extracted feature in Block S230;estimating a patient blood loss based on the blood volume indicator inBlock S240, estimating a euvolemic patient hematocrit based on anestimated patient blood volume and the estimated patient blood loss inBlock S250; receiving a measured patient hematocrit in Block S260; andgenerating a volemic status indicator based on a comparison between themeasured patient hematocrit and the estimated euvolemic patienthematocrit in Block S270.

Generally, second method S200 implements techniques described above toestimate a volume of patient blood loss over time through imageprocessing of physical samples containing blood and to output a volemicstatus indicator that defines a metric of patient intracirculatory bloodstatus. Second method S200 calculates the volemic status indicator basedon a comparison between a measured patient hematocrit and an estimatedeuvolemic patient hematocrit (i.e., a quantitative difference betweenthese values). The estimated euvolemic patient hematocrit can be basedon a previous (measured or estimated) patient hematocrit, an estimated(initial or recent) patient blood volume, and an estimated patient bloodloss, wherein the euvolemic patient hematocrit defines an estimatedintracirculatory hematocrit of the patient if the estimated volume ofpatient blood loss is replaced perfectly with saline. The measuredpatient hematocrit can be a tested (e.g., actual) patient hematocritvalue, such as measured with a non-invasive optical intracirculatoryhematocrit monitor outputting measured hematocrit values on a regulartime interval or measured by centrifuging a sample of the patient'sblood. Patient hematocrit can also be stated in terms of the patienthemoglobin concentration (e.g., HGB (g/dL)), which is experimentallycorrelated with patient hematocrit according to a mean corpuscularvolume (MCV) of the patient's erythrocytes. Second method S200 thereforegenerates the volemic status indicator that defines a magnitude ofpatient deviation from euvolemia. The volemic status indicator candefine a composite of the effectiveness of intravenous fluidreplenishment and insensible intracirculatory fluid losses (e.g.,internal bleeding, sweating, evaporation from open incisions) of thepatient.

Similar to first method S100 described above, second method S200 cantherefore be useful to a surgeon, an anesthesiologist, a nurse, etc. inan operating room and/or during a surgery to manage fluid replacementand/or blood component therapy.

As shown in FIGS. 11 and 12, Block S210 of second method S200 recitesreceiving an image of a physical sample. Generally, Block S210 canimplement techniques similar to Block S120 described above. For example,Block S210 can receive a static color image of the physical sample froman optical sensor, wherein the physical sample is one of a surgicalgauze sponge, a surgical towel, a surgical dressing, a surgical suctioncanister, and a cell salvage canister. However, Block S210 can functionin any other way to receive an image of the physical sample.

As shown in FIGS. 11 and 12, Block S220 of second method S200 recitesextracting a feature from an area of the image corresponding to thephysical sample. Generally, Block S220 can implement techniques similarto Block S130 described above. For example, Block S220 can extract acolor intensity value of a set of pixels within the area of the image.However, Block S220 can function in any other way to extract any otherfeature from any area of the image.

As shown in FIGS. 11 and 12, Block S230 of second method S200 recitesestimating a blood volume indicator of the physical sample according tothe extracted feature. Generally, Block S230 can implement techniquessimilar to Block S140 and Block S150 described above. Block S230 canestimate the blood volume indicator that includes a volume or otherquantitative measure of the hemoglobin content of the physical sample, avolume or other quantitative measure of the red blood cell content ofthe physical sample, a volume or other quantitative measure of blood thephysical sample, a volume or other quantitative measure of the plasmacontent of the physical sample, a volume or other quantitative measureof the white blood cell content of the physical sample, or any other avolume or other quantitative measure of blood or a blood componentwithin the physical sample. For example, as described above, Block S230can implementing a parametric model to transform a color intensity value(extracted in Block S220) into a quantity of red blood cells in thephysical sample. However, Block S230 can function in any other way toestimate any type or form of blood volume indicator in the physicalsample in any other suitable way.

As shown in FIGS. 11 and 12, Block S240 of second method S200 recitesestimating a patient blood loss based on the blood volume indicator.Generally, Block S240 can implement techniques similar to Block S150described above. In one implementation described above, Block S230estimates a mass of hemoglobin, in a surgical gauze sponge, and BlockS240 estimates the volume of blood in the surgical gauze sponge byconverting the estimated hemoglobin mass to a red blood cell volume, andconverting the estimated red blood cell volume to blood volume accordingto a known or estimated hematocrit of the patient. In thisimplementation, Block S240 can apply a static estimated hematocrit, amost-recently estimated patient hematocrit, an estimated hematocritaround an estimated use time of the surgical sponge (e.g., based on animage time stamp), or a recently measured hematocrit value (e.g.,measured with a non-invasive optical intracirculatory hematocritmonitor) to convert the estimated red blood volume to an estimatedextracorporeal blood volume. In another implementation, Block S230estimates the volume of red blood cells in a surgical suction canister,and Block S240 estimates the volume of blood in the suction canisterover time by applying a time-dependent hematocrit value to eachsubsequent image of the suction canister as the suction canister fillsover time.

Block S240 can also update a total estimated blood loss volume of thepatient with the estimated blood volume in the physical sample. Forexample, Block S240 can maintain a running total of patient blood lossby adding the estimated volume of blood in each subsequent physicalsample to a previous total. As shown in FIG. 14, Block S240 can alsooutput a time-dependent chart (e.g., graph) of total patient blood lossincluding the estimated patient blood loss and an aggregate of estimatedblood volumes of a set of previous physical samples. However, Block S240can function in any other way to estimate a patient blood loss based onthe blood volume indicator of the physical sample.

As shown in FIGS. 11 and 12, Block S250 of second method S200 recitesestimating a euvolemic patient hematocrit based on an estimated patientblood volume and the estimated patient blood loss. Generally, Block S250functions to estimate a euvolemic hematocrit of the patient byestimating the hematocrit of the patient if the total estimated volumeof patient blood loss were replaced with saline. Block S250 thereforeoutputs a hypothetical value of patient hematocrit that assumes constantintracirculatory blood volume (i.e., euvolemia) in which patienteuvolemia is maintained through infusion of crystalloid (i.e. saline).In particular, as blood, including red blood cells, is lost and theblood volume loss is replenished with crystalloid, the patient'sintracirculatory blood is diluted, and Block S250 outputs a measure ofthis hypothetical hemodilution in the form of an estimated euvolemicpatient hematocrit. In one example, Block S250 functions to estimate aeuvolemic hematocrit of the patient by estimating the hematocrit of thepatient according to a euvolemic blood model specifying intracirculatoryreplenishment of a total estimated volume of patient blood loss withsaline.

In one implementation, Block S250 subtracts the estimated blood volumein the physical sample from a previous estimated total intracirculatoryblood volume of the patient to calculate a new total intracirculatoryblood volume, converts the new total intracirculatory blood volume intoan intracirculatory red blood cell volume (or mass) according to aprevious patient hematocrit, and divides the intracirculatory red bloodcell volume by the sum of the original total intracirculatory bloodvolume (i.e., the sum new total intracirculatory blood volume and thevolume of saline replenishment) to estimate the euvolemic patienthematocrit. Alternatively, because hemoglobin mass and red blood cellvolume are correlated, Block S250 can similarly convert the new totalintracirculatory blood volume into an intracirculatory hemoglobin massaccording to a previous patient hematocrit and divide theintracirculatory hemoglobin mass by the sum of the original totalintracirculatory blood volume to estimate the euvolemic patienthematocrit. In this alternatively, Block S250 can output euvolemicpatient hematocrit in the form of hemoglobin mass per (deci-) liter ofblood (i.e., HGB g/dL).

Therefore, Block S250 can further receive a measured patient hematocritfrom an optical intracirculatory hematocrit monitor coupled to thepatient (e.g., arranged on a patient's finger). Block S250 canalternatively receive the measured patient hematocrit from an electronicmedical record (e.g., laboratory-run hematocrit), or from a bedsidespot-check device (e.g. an intraoperative assay of hematocrit), andBlock S250 can receive the measured patient hematocrit continuously, orsemi-continuously, on a constant or varying interval, or according toany other schedule.

Alternatively, Block S250 can implement a previously-estimated patienthematocrit or estimate an initial patient hematocrit according to aweight of the patient, a height of the patient, a gender of the patient,an age of the patient, and/or a health status of the patient, etc. BlockS250 can similarly estimate the total patient intracirculatory bloodvolume according to the weight of the patient, the height of thepatient, the gender of the patient, the age of the patient, and/or thehealth status of the patient, etc. Block S250 can alternatively receivean estimated total patient intracirculatory blood volume, such as froman anesthesiologist through a touch display following a usertracer-based intracirculatory blood volume test, or implement anintracirculatory patient hematocrit from a previous timestep. However,Block S250 can function in any other way to estimate a euvolemic patienthematocrit.

As shown in FIGS. 11, 12, and 15, Block S260 of second method S200recites receiving a measured patient hematocrit. Generally, Block S260functions to collect a real (i.e., measured, actual) hematocrit of thepatient such that Block S270 can compare the estimated euvolemichematocrit to a real hematocrit of the patient. For example, Block S260can interface with an optical intracirculatory hematocrit monitor toreceive measured patient hematocrits from the optical intracirculatoryhematocrit monitor on a regular time interval, such as every thirtyseconds. Alternatively, Block S260 can receive manual measuredhematocrit entries, such as entered by a nurse through a user interfacedisplayed on a touchscreen arranged within an operating room. However,Block S260 can function in any other way to receive a measuredhematocrit of the patient.

As shown in FIGS. 11 and 12, Block S270 of second method S200 recitesgenerating a volemic status indicator based on a comparison between themeasured patient hematocrit and the estimated euvolemic patienthematocrit. Generally, Block S270 subtracts the estimated euvolemichematocrit from the measured hematocrit of the patient to calculate thevolemic status indicator (i.e., a magnitude of divergence fromeuvolemia). As described above, the magnitude of divergence fromeuvolemia can indicate an effectiveness of intravenous fluidreplenishment and quantify insensible intracirculatory fluid losses,such as from internal bleeding, sweating, or evaporation from an openincision.

As shown in FIG. 15, Block S270 can also output a time-dependent chartincluding the volemic status indicator and a set of previous volemicstatus indicators. Block S270 can display this chart to a user, such asthrough a touchscreen arranged within an operating room, to enable theuser to monitor blood quality, fluid infusion effectiveness, and otherblood related events over time. For example, an anesthesiologist canidentity internal bleeding based on an increasing magnitude ofdivergence from euvolemia over time despite saline or blood infusion.However, Block S270 can function in any other way to output a volemicstatus indicator of any other form.

As shown in FIGS. 13 and 14, one variation of second method S200includes Block S280, which recites tracking a quantity of a fluidadministered to the patient intravenously and estimating anintracirculatory patient hematocrit based on a previous patienthematocrit, the estimated patient blood loss, a quantity of a fluidadministered to the patient, and the estimated patient blood volume.Generally, Block S280 implements techniques similar to first method S100described above. In one implementation, Block S280 tracks intravenousadministration of allogeneic blood from a blood transfusion bag to thepatient and estimates the intracirculatory patient hematocrit based onthe hematocrit of allogeneic blood in the transfusion bag. In thisimplementation, Block S280 can further receive a second image of theblood transfusion bag, extract a color-related feature from an area ofthe second image corresponding to the blood transfusion bag, andestimate the hematocrit of allogeneic blood in the blood transfusion bagbased on the color-related feature. As described above, Block S280 canalso track administration of a crystalloid fluid, of a knowncomposition, to the patient and can estimate the hematocrit of thepatient further based on the known composition of the crystalloid fluid.

As shown in FIG. 15, Block S280 can further plot the estimatedintracirculatory patient hematocrit with a set of previous estimatedintracirculatory patient hematocrits in a first time-dependent chart.Block S280 can additionally or alternatively plot the volume of fluidadministered to the patient in a second time-dependent chart. However,Block S280 can function in any other way to track a quantity of a fluidadministered to the patient and to estimate an intracirculatory patienthematocrit, and Block S280 can output any other this blood-related datain any other suitable format.

As shown in FIGS. 13 and 15, one variation of second method S200includes Block S282, which recites extrapolating a volume of insensiblepatient fluid loss from a difference between the estimatedintracirculatory patient hematocrit and the measured patient hematocrit.Generally, Block S282 functions to subtract the estimatedintracirculatory patient hematocrit from the measured patienthematocrit, which can provide a quantitative measure of divergence froman expected patient hematocrit. In particular, Blocks of second methodS200 track volumes of saline infusion, blood transfusion, blood loss,and/or other quantitative exchanges of fluids of known or estimatedqualities, and Block S280 estimates an intracirculatory patienthematocrit based on these known factors. Block S282 subsequentlycompares the measured and estimated intracirculatory patienthematocrits, which can provide insight into an intracirculatory bloodstatus of the patient. For example, a large divergence between themeasured and estimated intracirculatory patient hematocrits can indicatethat the patient is bleeding internally, and Block S290 can trigger analarm in response to such divergence that exceeds a thresholddivergence. Divergence between the measured and estimatedintracirculatory patient hematocrits can also indicate a rate ofevaporation from an open incision, a rate of absorption of fluid out ofthe bloodstream (which can indicate a hydration level), internalbleeding, pooling, etc.

Generally, Block S282 can extrapolate a measure of regulation and/orloss of fluid out of the patient's intravascular space, including acomparison of total blood volume and peripheral venous blood volume, anda measured hematocrit received in Block S260 (i.e., a peripheral venoushematocrit) can indicate red blood cell concentration in the patient'svascular system. As part of a body's natural fluid regulation, apatient's body may experience hemoconcentration and/or hemodilutionbetween the vascular system and interstitial spaces. For example, duringheaving lifting or a work out, a muscle may “pump” as the body sendsblood into peripheral spaces to aid a muscle recovery, which engorgesthe muscle, and hematocrit in the region of the muscle may thereforeshift due to internal fluid replacement. Similarly, during acutehemorrhage, a body may replace fluid internally and local to thehemorrhage, and Block S250 can output the volemic status indicator thatincorporates a body's fluid dynamics into one metric that is deviationfrom euvolemia. By comparing the estimated intracirculatory patienthematocrit and the measured patient hematocrit, Block S282 can thusoutput a quantitative measure of a patient's body dynamics.

As shown in FIGS. 13 and 15, one variation of second method S200includes Block S284, which recites estimating an effectiveness of thefluid administered to the patient to replenish blood volume losses basedon a difference between the estimated intracirculatory patienthematocrit and the estimated euvolemic patient hematocrit. Generally,Block S282 subtracts the estimated intracirculatory patient hematocritfrom the estimated euvolemic patient hematocrit to output a quantitativevalue of the effectiveness of a fluid infusion (e.g., saline) tomaintain patient euvolemia.

As shown in FIG. 13, one variation of second method S200 includes BlockS290, which recites triggering an alarm in response to a volemic statusindicator that exceeds a threshold volemic status indicator. Generally,Block S290 can trigger an alarm if the volemic status indicator thatexceeds a threshold volemic status indicator, which can indicate thatthe quality of intracirculatory blood of the patient has exceeded anacceptable volume or quality change. As described above, Block S290 cansimilarly trigger an alarm in response to divergence between measuredand estimated intracirculatory patient hematocrits that exceeds athreshold divergence. Block S290 can trigger an audible, visual, orother suitable alarm. Block S290 can also trigger administration of asaline infusion, a blood transfusion, a plasma infusion, etc. However,Block S290 can respond to a volemic status indicator (or other bloodstatus indicator) in any other suitable way.

As shown in FIG. 13, one variation of second method S200 includes BlockS292, which recites predicting a future volemic status indicatoraccording a difference between the volemic status indicator and aprevious volemic status indicator and triggering an alarm in response tothe previous volemic status indicator that exceeds a threshold volemicstatus indicator. Generally, Block S292 functions to extrapolate afuture volemic status based on two or more previous volemic statusindicators. Similar to Block S290, Block S292 can compare the predictedvolemic status indicator with a volemic status indicator threshold andtrigger an alarm or respond in any other suitable way in response to thepredicted volemic status indicator that exceeds the volemic statusindicator threshold. However, Block S292 can function in any other wayto predicting a future volemic status indicator

3. System

As shown in FIG. 9, a system 100 for managing blood loss of a patientincludes: an interface 102 that receives data related to intravenousadministration of a fluid to the patient; an optical sensor 110; aprocessor 120 coupled to the optical sensor 110 and to the interface102; a software module 122 executing on the processor 120 andinstructing the optical sensor 110 to capture an image of a physicalsample, the software module 122 further instructing the processor 120 totrack a quantity of the fluid administered to the patient according todata received by the interface 102, to estimate a red blood cell contentof the physical sample based on a feature extracted from the image, andto estimate a hematocrit of the patient based on a previous hematocritof the patient, the fluid administered to the patient, and the estimatedred blood cell content of the physical sample; and a display 130 coupledto the processor 120 and receiving instruction from the software module122 to display the estimated hematocrit of the patient.

The system 100 functions to implement first method S100 described above,wherein the optical sensor (e.g., camera) implements Block S122 tocapture the image of the canister and the processor 120 implementsBlocks S110, S120, S130, S140, S150, S160, S220, S240, S260, etc.described above according to the software module 122 to continuouslyand/or cyclically estimate a current intravascular hematocrit of thepatient. A surgeon, nurse, anesthesiologist, gynecologist, doctor,soldier, or other user can implement the system 100 to track theintravascular hematocrit of the patient over time, such as during asurgery, childbirth, or other medical event. The system 100 can alsodetect presence of blood in the physical sample(s), compute patientblood loss rate, estimate patient risk level (e.g., hypovolemic shock),determine hemorrhage classification of a patient. However, the system100 can perform any other suitable function.

As shown in FIGS. 9 and 10, the system 100 can be a handheld (e.g.,mobile) electronic device, such as a smartphone or tablet running animage-based blood estimation application (or app) and including theoptical sensor 110, the processor 120, and the display 130.Alternatively, the components of the system 100 can be substantiallydiscreet and distinct (i.e., not contained within a single housing). Forexample, the optical sensor 110 can be a camera arranged substantiallypermanently within an operating room, wherein the camera communicateswith a local network or a remote server (including the processor 120) onwhich the image of the canister is analyzed (e.g., according to firstmethod S100), and wherein a display 130 that is a computer monitor, atelevision, or a handheld (mobile) electronic device accesses anddisplays the output of the processor 120. The interface 102 can furtherbe embodied as touch sensor component of the display 130 of theelectronic device that includes a touchscreen. However, the system 100can be of any other form and/or include any other component.

The system 100 can be used in a variety of settings, including in ahospital setting, such as in a surgical operating room, in a clinicalsetting, such as in a delivery room, in a military setting, such as on abattlefield, or in a residential setting, such as to aid a consumer inmonitoring blood quality and quantity during menorrhagia (heavymenstrual bleeding) or epistaxis (nosebleeds). However, the system 100can be applicable to any other setting.

The interface 102 of the system 100 functions to receive data related tointravenous administration of a fluid to the patient. Generally, theinterface 102 defines an input region through which a user can enterinfusion and/or transfusion details. For example, a nurse, surgeon, oranesthesiologist can enter, through the interface 102, the type, totalvolume, flow rate, and start time of a crystalloid or colloid fluidadministered to the patient intravenously, as shown in FIG. 10.

The interface 102 can be any suitable type of interface. In one example,the interface 102 is a keyboard connected to a local computer (e.g.,desktop or laptop computer) containing the processor 120, the opticalsensor 110, and the display 130. Alternatively, the interface 102 can bea sensor component of a touchscreen integrated into a mobile electronicdevice (e.g., a smartphone, a tablet), wherein the electronic deviceincludes the optical sensor 110 and the processor 120, and wherein theoutput portion of the touchscreen defines the display 130. The interface102 can alternatively interface with an IV drip sensor to estimate theflow rate of fluid administered to the patient, and the interface canfurther receive an image of an IV bag and analyze the image to estimatethe volume and/or contents of the IV bag, as described above. However,the interface 102 can function in any other way to receive manual orautomatic entry of data pertaining to fluid administered to the patientintravenously. The interface 102 can further receive data pertaining toadministration of multiple different fluids simultaneously or in series,such as a simultaneous administration of saline and red blood cells orconsecutive administration of saline and then blood.

The optical sensor 110 of the system 100 functions to capture the imageof the physical sample. Generally, the optical sensor 110 can implementBlock S122 of first method S100 described above, as controlled by thesoftware module 122. In one example implementation, the optical sensor110 is a digital camera that captures a color image of the physicalsample or an RGB camera that captures independent image components inthe red, green, and blue component spaces. However, the optical sensor110 can be any number and/or type of cameras, charge-coupled device(CCD) sensors, complimentary metal-oxide-semiconductor (CMOS) activepixel sensors, or optical sensors of any other type. However, theoptical sensor 110 can function in any other way to capture the image ofthe physical sample, such as in any suitable form or across any suitablevisible or invisible spectra.

In one implementation, the optical sensor 110 is a camera arrangedwithin a handheld electronic device. In another implementation, theoptical sensor 110 is a camera or other sensor configured to be mountedon a pedestal for placement in an operating room, configured to mount toa ceiling over an operating table, configured for attachment to abattlefield helmet of a field nurse, configured to mount to a standalonehematocrit estimation system including the interface 102, the processor120, the display 130, and a staging tray that supports the physicalsample for imaging, or configured for placement in or attachment to anyother object or structure.

According to instructions from the software module 122, the processor120 of the system 100 tracks a quantity of the fluid administered to thepatient according to data received by the interface 102, estimates a redblood cell content of the physical sample based on a feature extractedfrom the image, and estimates a hematocrit of the patient based on aprevious hematocrit of the patient, the fluid administered to thepatient, and the estimated red blood cell content of the physicalsample. Generally, the processor 120 implements one or more Blocks offirst method S100 described above according to instructions from thesoftware module 122. The processor 120 can further repeat execution ofthese instructions in response to additional images of additionalphysical samples over time to generate a trendline of estimated patienthematocrit over time. The processor can therefore receive and analyzeimages of any one or more suitable types (e.g., static, streaming, MPEG,.JPG, .TIFF) and/or images from one or more distinct cameras or opticalsensors.

The processor 120 can be coupled to the optical sensor 110, such as viaa wired connection (e.g., a trace on a shared PCB) or a wirelessconnection (e.g., a Wi-Fi or Bluetooth connection), such that theprocessor 120 can access the image of the physical sample captured bythe optical sensor 110 or visible in the field of view of the opticalsensor 110. In one implementation, the processor 120 is arranged withina handheld electronic device that also contains the optical sensor 110and the display 130. In another implementation, the processor 120 is aportion of or is coupled to a remote server, wherein image data from theoptical sensor 110 is transmitted (e.g., via an Internet or localnetwork connection) to the processor 120 that is remote, wherein theprocessor 120 estimates the extracorporeal red blood cell content and/orblood volume in the physical sample and estimates the hematocrit of thepatient based on the estimated red blood cell content and/or bloodvolume, and wherein the hematocrit estimate is transmitted to thedisplay 130.

In one implementation and as described above, the processor 120 canestimate the red blood cell content in the physical sample by matching aportion of the image of the physical sample to a template image, whereinthe template image is one template image in a library of templateimages. For example, the system 100 can further include a data storagemodule 160 configured to store a library of template images of knowncontents and/or concentrations of the red blood cells. In thisimplementation, the processor can correlate the extracted features withthe red blood cell count by comparing a feature extracted from the imagewith a template image in the library of template images, as describedabove. Alternatively and as described above, the processor 120 canimplement a parametric model to estimate the red blood cell content inthe physical sample based on the feature extracted from the image.

The processor 120 is further coupled to the interface 102 to receivedata pertaining to administration of fluid to the patient over time. Forexample, the processor 120 can receive, from the interface 102, thetype, total volume, flow rate, and start time of administration of afluid into the patient. From this initial data, the processor 120 canintegrate the flow rate over time, in light of the composition of theadministered fluid and the total volume of the IV bag, to determine thetotal volume, parts, mass, weight, etc. of fluid, blood cells,electrolytes, etc. delivered to the patient up to any given time.

The processor 120 further implements Block S160 described above toaggregate multiple variables related to, directly affecting, and/orindirectly affecting a quality and/or quantity of the patient'sintravascular blood volume. As described above, such variables caninclude fluid (e.g., blood plasma, saline) administered to the patient,blood lost by the patient, non-blood fluids lost or excreted by thepatient, adsorption of fluid and electrolytes into or out of thecirculatory system, etc., any of which can be dependent on time, as wellas initial patient hematocrit, an initial blood volume, a composition ofa colloid or crystalloid administered to the patient, etc. The processorcan therefore continuously and/or cyclically estimate the hematocrit ofthe patient over time in light of further blood loss (e.g., asdetermined through analysis of images of additional physical samples)and further intravenous administration of fluids (e.g., based on datacollected by the interface 102).

The software module 122 of the system 100 functions to control theinterface 102, the optical sensor 110, the processor 120, and thedisplay 130 to receive patient IV data, capture the image of thephysical sample, analyze the image, and estimate the hematocrit of thepatient. The software module 122 can execute on the processor 120 as anapplet, a native application, firmware, software, or any other suitableform of code to control processes of the system 100. Generally, thesoftware module controls implementation of Blocks of first method S100described above within the system 100, though the software module 122can control and/or implement any other suitable process or method on orwithin the system 100.

In one example application, the software module 122 is a nativeapplication installed on the system 100 that is a handheld (i.e.,mobile) computing device, such as a smartphone or tablet. When selectedfrom a menu within an operating system executing on the computingdevice, the software module 122 opens, interfaces with a user toinitialize a new case, receives IV data through the interface 102 thatincludes a touchscreen, controls the optical sensor 110 integrated intothe computing device to capture the image of the physical sample,implements machine vision and executes mathematical algorithms on theprocessor 120 to estimate the quantity of red blood cells in thephysical sample and the hematocrit of the patient, and controls thedisplay 130 to render the estimated current hematocrit of the patient,as shown in FIG. 9. However, the software module 122 can be of any otherform or type and can be implemented in any other way.

The display 130 of the system 100 renders the estimated hematocrit ofthe patient. The display 130 can be arranged within the handheldelectronic device (e.g., smartphone, tablet, personal data assistant)that also contains the optical sensor 110 and the processor 120. Thedisplay 130 can also be physically coextensive with the interface, suchas in one implementation of the system 100 that is a handheld electronicdevice including a touchscreen. Alternatively, the display can be acomputer monitor, a television screen, or any other suitable displayphysically coextensive with any other device. The display 130 can be anyof an LED, OLED, plasma, dot matrix, segment, e-ink, or retina display,a series of idiot lights corresponding to estimated hematocrit ranges,or any other suitable type of display. The display 130 can further be incommunication with the processor 120 via any of a wired and a wirelessconnection.

The display 130 can also render an estimated quantity of the bloodand/or red blood cell content of one or more physical samples, a currenttotal estimated red blood cell loss or blood loss of the patient, acurrent estimated intravascular blood volume or percentage blood volumeof the patient, a hemorrhage rating or risk of the patient, or any othersuitable blood-related variable or parameter of the patient. Asdescribed in U.S. patent application Ser. No. 13/738,919, this data canbe presented in the form of a dynamic augmented reality overlay on topof a live video stream of an operating room and/or an imaging area forphysical samples. For example, images from the optical sensor 110 can berelayed substantially in real time, through the processor 120, to thedisplay 130 wherein the images are rendered concurrently with a currentestimated hematocrit of the patient, an estimated blood volume of arecent physical sample, and a total estimated patient blood loss. Thedisplay 130 can also render any of the foregoing or other data in table,chart, or graphical form, such as multiple time-dependent hematocritestimates. The display 130 can also render any of an image of a previousphysical sample, a patient risk level (e.g., risk of hypovolemic shock),a hemorrhage classification of the patient, and/or a warning orsuggestion, such as to begin a blood transfusion. Any of these data,warnings, and/or suggestions can also be depicted across multiplescreens or menus or made accessible through the display 130 and/orinterface 102 in any other suitable way.

As shown in FIG. 9, one variation of the system 100 further includes analarm module 170, wherein the software module further triggers the alarmmodule in response to the estimated hematocrit of the patient fallingoutside a predefined range of suitable hematocrit values. Generally, thealarm module 170 functions to audibly and/or visually alert a user, suchas a surgeon, anesthesiologist, or nurse, when a risk level of thepatient falls outside of a suitable range. For example, the alarm module170 can include a speaker, buzzer, or other type of sound driver. Inanother example, the alarm module 170 includes a digital alarm modulethat is rendered on the display, such as a flashing red box around apatient hematocrit value rendered on the display 130 or a largetext-based warning that is displayed over the other data rendered on thedisplay 130.

In one example implementation, the software module 122 triggers thealarm module 170 in response to an estimated patent hematocrit thatfalls outside a predefined range of safe hematocrit values, which can bebased on the age, gender, health status, demographic, etc. of thepatient. For example, the predefined range of safe hematocrit values fora male patient between the ages of 20 and 60 can be 0.39 to 0.50,whereas the predefined range of safe hematocrit values for a femalepatient between the ages of 20 and 60 can be 0.36 to 0.44. In anotherexample implementation, the software module 122 triggers the alarmmodule 170 in response to an estimated total patient red blood cell lossthat falls outside a predefined maximum percentage of loss of theestimated total initial red blood cell count of the patient. Forexample, the software module 122 can trigger the alarm module when thepatient has lost more than 20% of his initial red blood cell count. Inyet another example implementation, the software module 122 triggers thealarm module 170 in response to an estimated increase in intravascularfluid volume of patient that exceeds a threshold increase over theinitial patient blood volume, such as a 10% increase over the initialpatient blood volume. However, the software module 122 can trigger thealarm module 170 in response to any other blood-related value.

As shown in FIG. 10, one variation of the system 100 further includes ahandheld housing 140 configured to contain the optical sensor 110, theprocessor 120, and the display 130. The handheld housing 140, withoptical sensor 110, processor 120, and display 130, can define ahandheld (mobile) electronic device capable of estimating patienthematocrit in any number of suitable environments, such as in anoperating room or a delivery room. The handheld housing 140 can be of amedical-grade material such that the system 100 that is a handheldelectronic device can be suitable for use in an operating room or othermedical or clinical setting. For example, the housing can bemedical-grade stainless steel, such as 316L stainless steel, amedical-grade polymer, such as high-density polyethylene (HDPE), or amedical-grade silicone rubber. However, the housing can be of any othermaterial or combination of materials.

As shown in FIG. 10, one variation of the system 100 includes a wirelesscommunication module 150 that communicates the estimated quantity redblood cell content of the physical sample, the estimated blood volume ofthe physical sample, and/or the estimate patient hematocrit to a remoteserver that stores and maintains an electronic medical record of thepatient. The system 100 can also update the medical record withestimated blood loss over time, patient risk level, hemorrhageclassification, and/or other blood-related metrics. The patient medicalrecord can therefore be updated substantially automatically during amedical event, such as during a surgery or childbirth.

The systems and methods of the embodiments can be embodied and/orimplemented at least in part as a machine configured to receive acomputer-readable medium storing computer-readable instructions. Theinstructions are executed by computer-executable components integratedwith the system 100, the optical sensor, the processor, the display,hardware/firmware/software elements of a system or handheld computingdevice, or any suitable combination thereof. Other systems and methodsof the embodiments can be embodied and/or implemented at least in partas a machine configured to receive a computer-readable medium storingcomputer-readable instructions. The instructions are executed bycomputer-executable components integrated by computer-executablecomponents integrated with apparatuses and networks of the typedescribed above. The computer-readable medium can be stored on anysuitable computer readable media such as RAMs, ROMs, flash memory,EEPROMs, optical devices (CD or DVD), hard drives, floppy drives, or anysuitable device. The computer-executable component is a processor butany suitable dedicated hardware device can (alternatively oradditionally) execute the instructions.

As a person skilled in the art will recognize from the previous detaileddescription and from the figures and claims, modifications and changescan be made to the preferred embodiments of the invention withoutdeparting from the scope of this invention defined in the followingclaims.

What is claimed is:
 1. A method comprising: estimating, by one or moreprocessors, an initial blood volume from a weight and height of apatient; estimating, by the one or more processors, a firstextracorporeal blood content of one or more physical samples; accessing,by the one or more processors, an initial hematocrit of the patient;estimating, by the one or more processors, a first blood lost volumefrom the first extracorporeal blood content and the initial hematocrit;estimating, by the one or more processors, a first updated hematocrit ofthe patient based on the initial hematocrit of the patient, the initialblood volume of the patient and the first blood lost volume; estimating,by the one or more processors, a second extracorporeal blood content ofone or more physical samples; and estimating, by the one or moreprocessors, a second blood lost volume from the second extracorporealblood content and the first updated hematocrit.
 2. The method of claim1, further comprising: estimating, by the one or more processors, asecond updated hematocrit of the patient based on the first updatedhematocrit of the patient, the initial blood volume of the patient andthe first and second blood lost volumes.
 3. The method of claim 1wherein the initial blood volume is estimated from the height, weightand a gender of the patient.
 4. The method of claim 1, furthercomprising: determining, by the one or more processors, that a quantityof a fluid is intravenously provided to a patient; and estimating, bythe one or more processors, a second updated hematocrit of the patientbased on the first updated hematocrit of the patient and the quantity ofthe fluid intravenously provided to the patient.
 5. The method of claim1, wherein the estimating of an extracorporeal blood content of aphysical sample includes: accessing an image of the physical sample;extracting a feature from the image of the physical sample; andestimating the extracorporeal blood content of the physical sample basedon the feature extracted from the image of the physical sample.
 6. Themethod of claim 1, further comprising: causing presentation of an alarmbased on a total blood lost volume.
 7. The method of claim 1, furthercomprising: determining, by the one or more processors, that a quantityof a blood transfusion is provided to the patient; and estimating, bythe one or more processors, a second updated hematocrit of the patientbased on the first updated hematocrit of the patient, a hematocrit ofthe blood transfusion, and the quantity of the blood transfusion.
 8. Asystem comprising: one or more processors; and a computer-readablemedium storing instructions that, when executed by the one or moreprocessors, causes the one or more processors to perform operationscomprising: estimating an initial blood volume from a weight and heightof a patient; estimating a first extracorporeal blood content of one ormore physical samples; accessing an initial hematocrit of the patient;estimating a first blood lost volume from the first extracorporeal bloodcontent and the initial hematocrit; estimating a first updatedhematocrit of the patient based on the initial hematocrit of thepatient, the initial blood volume of the patient and the first bloodlost volume; estimating a second extracorporeal blood content of one ormore physical samples; and estimating a second blood lost volume fromthe second extracorporeal blood content and the first updatedhematocrit.
 9. The system of claim 8, wherein the operations furthercomprise: estimating a second updated hematocrit of the patient based onthe first updated hematocrit of the patient, the initial blood volume ofthe patient and the first and second blood lost volumes.
 10. The systemof claim 8, wherein the initial blood volume is estimated from theheight, weight and a gender of the patient.
 11. The system of claim 8,wherein the operations further comprise: determining that a quantity ofa fluid is intravenously provided to a patient; and Estimating a secondupdated hematocrit of the patient based on the first updated hematocritof the patient and the quantity of the fluid intravenously provided tothe patient.
 12. The system of claim 8, wherein the operation ofestimating an extracorporeal blood content of a physical sampleincludes: accessing an image of the physical sample; extracting afeature from the image of the physical sample; and estimating theextracorporeal blood content of the physical sample based on the featureextracted from the image of the physical sample.
 13. The system of claim8, wherein the operations further comprise: causing presentation of analarm based on a total blood lost volume.
 14. The system of claim 8,wherein the operations further comprise: Determining that a quantity ofa blood transfusion is provided to the patient; and Estimating a secondupdated hematocrit of the patient based on the first updated hematocritof the patient, a hematocrit of the blood transfusion, and the quantityof the blood transfusion.
 15. A computer-readable medium storinginstructions that, when executed by one or more processors, causes theone or more processors to perform operations comprising: estimating aninitial blood volume from a weight and height of a patient; estimating afirst extracorporeal blood content of one or more physical samples;accessing an initial hematocrit of the patient; estimating a first bloodlost volume from the first extracorporeal blood content and the initialhematocrit; estimating a first updated hematocrit of the patient basedon the initial hematocrit of the patient, the initial blood volume ofthe patient and the first blood lost volume; estimating a secondextracorporeal blood content of one or more physical samples; andestimating a second blood lost volume from the second extracorporealblood content and the first updated hematocrit.
 16. Thecomputer-readable medium of claim 15, wherein the operations furthercomprise: estimating a second updated hematocrit of the patient based onthe first updated hematocrit of the patient, the initial blood volume ofthe patient and the first and second blood lost volumes.
 17. Thecomputer-readable medium of claim 15, wherein the initial blood volumeis estimated from the height, weight and a gender of the patient. 18.The computer-readable medium of claim 15, wherein the estimating of anextracorporeal blood content of a physical sample includes: accessing animage of the physical sample; extracting a feature from the image of thephysical sample; and estimating the extracorporeal blood content of thephysical sample based on the feature extracted from the image of thephysical sample.
 19. The computer-readable medium of claim 15, whereinthe operations further comprise: causing presentation of an alarm basedon a total blood lost volume.
 20. The computer-readable medium of claim15, wherein the operations further comprise: determining, by the one ormore processors, that a quantity of a blood transfusion is provided tothe patient; and estimating, by the one or more processors, a secondupdated hematocrit of the patient based on the first updated hematocritof the patient, a hematocrit of the blood transfusion, and the quantityof the blood transfusion.